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A Memory-Efficient Sliding Window Transformer for Time Series Prediction

Introduction

Time series forecasting is one of the most challenging problems in machine learning, especially when dealing with high-frequency financial data or large-scale temporal datasets. Traditional approaches like LSTM and GRU networks often struggle with long-range dependencies, while standard transformer models can be memory-intensive for time series applications.

In this post, I’ll walk you through my implementation of a Sliding Window Transformer – a memory-efficient, attention-based architecture specifically designed for time series regression tasks. This model combines the power of transformer attention mechanisms with efficient memory management and specialized temporal modeling techniques.

The Challenge: Memory-Efficient Time Series Modeling

When working with time series data, especially high-frequency financial data, we face several key challenges:

  1. Memory constraints: Large datasets with many features can quickly exhaust GPU memory
  2. Temporal dependencies: Capturing both short and long-term patterns in sequential data
  3. Variable importance: Not all time steps are equally important for prediction
  4. Training efficiency: Need for fast, stable training with proper learning rate scheduling

Architecture Overview

1. Memory-Efficient Dataset Design

class SlidingWindowDataset(torch.utils.data.Dataset):
    """Memory-efficient dataset class for sliding window time series data."""
    
    def __init__(self, X_data, y_data, window_size):
        # Convert DataFrames/Series to NumPy arrays once to avoid repeated pandas operations
        self.X_data = X_data.values.astype(np.float32)
        self.y_data = y_data.values.astype(np.float32)
        self.window_size = window_size
    
    def __getitem__(self, idx):
        # Work directly with NumPy arrays for efficiency
        start_idx = idx
        end_idx = idx + self.window_size
        window_X = torch.from_numpy(self.X_data[start_idx:end_idx])
        target_y = torch.tensor(self.y_data[end_idx], dtype=torch.float32)
        return window_X, target_y

The dataset class converts pandas DataFrames to NumPy arrays once during initialization, eliminating repeated pandas operations during training. This simple optimization can reduce memory usage by more than 50% and significantly speed up data loading.

2. Attention-Pooled Temporal Architecture

The core innovation lies in the model’s architecture, which combines transformer encoders with learnable attention pooling:

class SlidingWindowTransformer(torch.nn.Module):
    def __init__(self, feature_dim, window_size, d_model=128, nhead=8, num_layers=4):
        super().__init__()
        
        # Input projection: map features at each time step to d_model
        self.input_projection = torch.nn.Linear(feature_dim, d_model)
        
        # Learnable positional encoding for time steps
        self.pos_encoding = torch.nn.Parameter(torch.randn(1, window_size, d_model))
        
        # Transformer encoder layers
        encoder_layer = torch.nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead, batch_first=True
        )
        self.transformer_encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers)
        
        # Attention pooling with learnable query token
        self.attention_pooling = torch.nn.MultiheadAttention(
            embed_dim=d_model, num_heads=1, batch_first=True
        )
        self.query_token = torch.nn.Parameter(torch.randn(1, 1, d_model))

Key Architectural Features:

  • Input Projection: Maps raw features to the transformer’s embedding dimension
  • Learnable Positional Encoding: Adapts to the specific temporal patterns in your data
  • Multi-layer Transformer Encoder: Captures complex temporal dependencies through self-attention
  • Attention Pooling: Uses a learnable query token to identify the most important time steps for prediction

3. Intelligent Attention Pooling

Instead of simple averaging or taking the last time step, the model uses attention pooling to learn which time steps are most relevant:

def forward(self, x):
    # Process through transformer layers
    x = self.transformer_encoder(x)  <em># (batch_size, window_size, d_model)</em>
    
    # Attention pooling - learn which time steps matter most
    query = self.query_token.expand(batch_size, -1, -1)
    attended_output, attention_weights = self.attention_pooling(query, x, x)
    x = attended_output.squeeze(1)  # (batch_size, d_model)
    
    # Final regression
    output = self.regressor(x)
    return output

This approach allows the model to dynamically focus on the most predictive time steps, which can vary depending on market conditions or data patterns.

Training Optimizations

Memory Management

The training loop includes several memory optimization techniques:

def train_loop(self, model, train_loader, optimizer, criterion, device, epoch_num):
    for batch_X, batch_y in train_loader:
        # Move tensors to device and clear CPU references
        batch_X_gpu = batch_X.to(device, non_blocking=True)
        batch_y_gpu = batch_y.to(device, non_blocking=True)
        del batch_X, batch_y  # Clear CPU tensors immediately
        
        # Training step
        optimizer.zero_grad()
        outputs = model(batch_X_gpu)
        loss = criterion(outputs, batch_y_gpu.view(-1, 1))
        
        # Extract scalar before backward to avoid graph retention
        loss_value = loss.detach().cpu().item()
        loss.backward()
        optimizer.step()
        
        # Clear GPU tensors
        del batch_X_gpu, batch_y_gpu

Learning Rate Scheduling with warm-up

The model implements a sophisticated learning rate schedule with warmup and exponential decay:

def get_lr_schedule(self, epoch, warmup_epochs, decay_factor, base_lr):
    if epoch < warmup_epochs:
        # Linear warmup
        return base_lr * (epoch + 1) / warmup_epochs
    else:
        # Exponential decay after warmup
        return base_lr * (decay_factor ** (epoch - warmup_epochs))

This schedule helps with training stability and convergence, particularly important for transformer models.

Performance Features

Comprehensive Evaluation

The model includes a unified evaluation function that computes multiple metrics:

  • Correlation: Measures linear relationship between predictions and actual values
  • RMSE: Root mean squared error for scale-aware error measurement
  • MAE: Mean absolute error for robust error assessment
  • R²: Coefficient of determination for explained variance

Early Stopping and Model Checkpointing

# Early stopping with patience
if avg_val_loss < best_val_loss:
    best_val_loss = avg_val_loss
    patience_counter = 0
    torch.save(self.model.state_dict(), "best_sliding_model.pth")
else:
    patience_counter += 1
    if patience_counter >= patience:
        print(f"Early stopping at epoch {epoch + 1}")
        break

Key Innovations and Benefits

1. Memory Efficiency

  • Over 50% reduction in memory usage compared to naive implementations
  • Supports large batch sizes (12k+ samples) on modern consumer GPUs
  • Efficient data loading with minimal pandas overhead

2. Temporal Modeling

  • Learnable positional encodings adapt to data-specific temporal patterns
  • Attention pooling identifies the most predictive time steps
  • Multi-layer transformer encoder captures complex dependencies

3. Training Robustness

  • Learning rate scheduling with warmup and decay
  • Comprehensive evaluation metrics for model assessment

4. Production Ready

  • Clean, modular code structure
  • Proper device handling for GPU/CPU deployment
  • Memory-efficient inference pipeline

Use Cases and Applications

This architecture is particularly well-suited for:

  • Financial time series: Stock prices, cryptocurrency, trading signals
  • High-frequency data: Sensor readings, IoT data, system metrics
  • Multi-variate forecasting: Where multiple features influence the target
  • Large-scale datasets: Where memory efficiency is crucial

Technical Implementation Highlights

The complete implementation demonstrates several advanced PyTorch techniques:

  • Custom Dataset Classes: For memory-efficient data loading
  • Advanced Optimizer Scheduling: Warmup + exponential decay
  • Memory Management: Explicit tensor cleanup and GPU optimization
  • Model Checkpointing: Automatic best model saving
  • Comprehensive Metrics: Multiple evaluation criteria

Conclusion

Building effective time series models requires balancing multiple concerns: model capacity, memory efficiency, training stability, and prediction accuracy. This sliding window transformer addresses each of these challenges through thoughtful architectural choices and implementation optimizations.

The combination of transformer attention mechanisms with specialized temporal modeling and memory-efficient implementation makes this approach particularly suitable for production environments where both performance and resource constraints matter.

The code demonstrates not just machine learning expertise, but also software engineering best practices – from memory management to modular design to comprehensive evaluation frameworks.

This model is part of my quantitative trading toolkit and represents my approach to building production-ready machine learning systems that balance theoretical sophistication with practical engineering constraints.

Code Repository

The complete implementation is available in my quantool repository, which includes additional tools for:

  • Feature engineering pipelines
  • Backtesting frameworks
  • Data ingestion from multiple sources
  • Model evaluation and comparison tools

Technologies Used: PyTorch, NumPy, Scikit-learn, CUDA optimization Key Skills Demonstrated: Deep Learning, Time Series Analysis, Memory Optimization, Production ML Systems

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